Journal of Plant Nutrition and Soil Science, August 2010, Vol.173(4), pp.517-524
The evaluation of the impact of additional soil pollutants has to be contrasted against the naturally occurring pollutant concentration, , the background concentration. Because background concentrations have to represent areal entities, point information has to be extrapolated into the area using interpolation methods. Thus, the accuracy of the interpolation method is crucial for the correct designation of background values to the areas. For the area of Bavaria (SE Germany), the actual background values of organic and inorganic soil pollutants were derived from 〉337,000 data from 5000 horizons based upon 1134 soil profiles. Background values were determined for the different soil depth compartments (O layer, topsoil, subsoil, and parent material) and land uses (agriculture, forestry). For interpolation between the nodes, Indicator Kriging was applied. The kriged total area was subdivided into 6 subareas of different background concentrations using percentile thresholds. To derive representative background concentrations, the reliable segregation of the total area into subareas and, thus, a robust interpolation method is a prerequisite. In this study, the robustness of the applied Indicator Kriging should be tested. Influences of data transformations and different kriging methods upon the demarcation of subareas should be investigated for the organic sum‐parameter EPA‐PAH. Neither a data transformation nor the comparison with Ordinary Kriging yielded significant deviations in the assigned subareas. Furthermore, cross‐validation as well as addition of synthetic noise was used to check the susceptibility of the method to artifacts and changes in the data set. After random splitting of the original data set into 4 subsets and re‐arrangement to 6 half‐sets, subsequent Indicator Kriging produced 6 results with mainly identical subarea configurations. Cross‐validation, , comparison of points from the kriging surface (validation data set) with the calibration data set, yielded considerable residuals between estimates and measurements. Based on these normally distributed residuals, random numbers with identical statistical moments were generated and used as measurement errors for another kriging run. This synthetic noise was added to the corresponding result based on the calibration half‐set. The resulting subareas changed only slightly for the most polluted region, but considerably for the other regions. The chosen interpolation method provides sufficient stability to demarcate the relevant areas with elevated pollution in Bavaria. For other areas, its stability is less clear. Here, additional soil samples are required.
Epa‐Pah ; Forest Organic Layer ; Bavaria ; Geostatistics ; Statistics ; Background Value